Thanks to a (very flattering) mention of my thesis project on Gizmodo after the ITP Spring Show, the use of Mobile Logger has quadrupled in the past two days. I had been watching the number of unique users rise on the Dashboard page, currently near 800…but then wondered what that would look like animated over time.

Here’s the world map, showing events pop up chronologically. There was the initial spread on April 12th from the public release in the app store…but just wait..wait…wait…for  May 12th. Fun!

Thanks Gizmodo (and Matt)!

(Here is the documentation for my thesis project at NYU’s Interactive Telecommunications Program. PDF version here.)

Riding through Mountains of Data:
Visualizations of Cycling

Robert Carlsen
Interactive Telecommunications Program
Tisch School of the Arts
New York University

Abstract

This project attempts to describe the cycling experiences of several riders in New York City through a series of visualizations. Specifically, I am interested to discover if riders similar to myself share a common experience through which a sense of connection could be derived.

Cyclists were encouraged to record their travels using their personal mobile devices running Mobile Logger, a custom iPhone application.
Log data was uploaded by the application to an online database in near real-time during each ride. This data was analyzed and filtered to provide source material for the resulting visualizations and system “dashboard” at http://mobilelogger.robertcarlsen.net.

Keywords

Cycling, New York City, sensors, iPhone, visualization, mapping, tracking, logging, mobile, application, bicycle

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The Mobile Logger application has been public for a couple of weeks and has (surprisingly, to me) been used in every continent, save Antarctica. I first noticed several events in the database from Australia, then the UK. I was mostly catching these events by coincidence when I was looking over my own data and wondered just where (in the world) these other users were logging from.

For Earth Day, I generated a map of the global users of Mobile Logger and put it on the status page. While the historical data is really neat, and humbling to know that people all over have tried this app, the real-time data is captivating. I added the city of the most recent event and a pulsing marker to the map. Now, the location of the newest log is marked when the status page is updated. Next, I’d like to show it when several events have been logged at the same time.

That’s it for now…working on the next iteration of the visualizations. I’m thinking of some Feltron-inspired summary charts, then a more detailed array of specific data. Who knows?!

Thursday, April 22nd is Earth Day. The weather is looking to be sunny and 65 degrees in New York City. Sounds like a perfect day to ride your bike (or walk, run or whatever you like to do outside). Since you’re already going to be out there, why not log the trip, help me with my thesis, and have your data made into some visualizations I’m preparing for the project?

It’s pretty simple…download Mobile Logger from the App Store (iPhone 3G/3GS), open it, then tap Logging switch to begin. Put the phone in your pocket, bag, mounted to handlebars, or wherever is convenient and go. You can double-tap the screen to disable the display, but shouldn’t lock the phone.

When you’ve reached your destination, tap the logging switch again to stop and you’re done! The log data is automatically uploaded to the Mobile Logger server and will be included in my research (this uploading can be disabled if you’d like to use the app without contributing to the project, too).

What I’m really interested in exploring is a sense of connection between us by sharing our experiences. I ride a bike daily through NYC, and encounter many other cyclists, walkers and drivers. We pass each other in a moment, or perhaps share a lane for a bit and then continue on our separate ways. How does my 5 mile, 25 minute ride from Greenpoint to the East Village compare to someone riding from Queens? What does a ride around Prospect Park share with one in Central Park? What’s the loudest part of the city for a cyclist? Where are the most frequently ridden routes?

I’ll be working with the contributed data to create visualizations which attempt to answer these questions. The “dashboard” of the system will be present at mobilelogger.robertcarlsen.net. More info about the app is available on it’s documentation page.

Times UP! is also organizing a ride at 7pm from Union Square if you still need another excuse to get on a bike, skates or a board. It would be neat to see a bunch of riders converge on a location, then ride together in a group. I really want to see what that visualization would look like…

Thanks, and enjoy the ride!

After several rounds of rejection, Mobile Logger has been accepted and is available on the App Store! Feel free to try it out; hopefully some folks will find it useful. The source code for the application has been released under the GPL and is available on github.

I’m still actively recruiting participants for my ongoing thesis project, which involves visualizing cyclists in New York City. If you’d like your riding to become incorporated in some pretty pictures to be presented in May, then by all means start logging (and thank you in advance)!

Be warned, it’s a battery hog. Feel free to let me know if it gives you any trouble.

Thesis Proposal Title
Where do we go now?

Thesis Statement
I will create a series of visualizations attempting to decipher the experiences of a large group of cyclists in New York City. The project has two principle components: data collection and analysis / visualization.

Personal Statement
Riding a bicycle provides me with a sense of self-reliance. It can provide transportation, fitness, employment and enjoyment. It’s faster than walking and more maneuverable than driving. In dense city congestion it can be faster than mass transit. However, we’re generally more exposed to the elements and danger than other traffic. What does this experience look like? How could it be recorded? Mobile sensors reflect a personal experience in a way that fixed sensors can only infer. Focusing on personal mobile devices as nodes in this network provides a priority on the experience of individuals.

What could we learn about ourselves and our world if there was a ubiquitous network of sensors collecting data about the environment as we experience it? Would analysis and visualization reveal trends and patterns in the aggregate behavior of participants in the network?

Research
Personal Environmental Impact Report (UCLA Cens) http://peir.cens.ucla.edu/
Flight Patterns, http://www.aaronkoblin.com/work/flightpatterns/
Cabspotting, http://cabspotting.org/
Copenhagen Wheel (MIT Sensible City)
CitySense (Sense Networks) http://www.citysense.com
Beautiful Data. Segaran & Hammerbacher. O’Reilly Media. 2009.
Open Data Consortium Project, http://www.opendataconsortium.org

Work Description
GPS-enabled mobile devices are becoming prevalent enough to use them for large-scale personal data collection. The data collection portion of this project will utilize a mobile logging application (initially iPhone and Android ) to record each rider’s experience. The application will upload data to a server for storage and later analysis. To facilitate ride data, I will organize a one-day event (“Log your ride to work day”?) or piggy-back on an existing event (critical mass, charity ride). Alternatively, I may organize a proof-of-concept event with a smaller group over a longer time, perhaps a week.

Post collection, I will analyze the data looking for relationships and trends among riders. This analysis will be critical for the eventual visualizations. I have an initial set of questions which I’m looking to answer: How many other riders are are near another rider? How far apart are they? How fast are they traveling? Respectively? How smooth is the ride? Are they rocking? Do they lean to one side? Do several riders experience similar conditions at the same place and time? Where do riders go? Where do they originate? Where do they congregate?

Visualizations of this data derive from further questions. What does a group ride look like? What if location was stretched along a time axis like a ribbon? My overarching goal with this project is to make these possibly abstract images be meaningful to uninitiated viewers.

The end product will primarily be this series of static and interactive visualizations using the collected data. Additionally, I’m aiming to publish the project’s process in the spirit of open source. This includes publishing the collected data for other analysis, releasing the logger application source code, documenting collection methods and describing the visualization process. Hopefully, this will enable other people to extend and augment the work in ways I haven’t envisioned.

Our assignment last week was to use Foursquare to log our daily travels. This week, we were asked to use a classmate’s Foursquare check-in history as the source of our visualizations. I was given Bryan Lence’s data and set off to see what was there.

Over the past few weeks I’ve been teaching myself the R “environment for statistical computing and graphics“. It’s an open source project and has a doubly steep learning curve (for me, at least) of an unfamiliar syntax and medium (statistics). I can see it’s power for visualizations, however, when used to reveal interesting associations which can be further refined in other graphics software (in this case, Illustrator).

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I fractured my ankle in a hard snowboard crash a couple of weeks ago and of course I was data logging the accelerometer forces. I was using the iPhone app developed last fall for the seismi{c}ycling project; while riding the phone was in my jacket’s internal chest pocket.

A group from ITP was enjoying the bitter weather at Mount Snow, in West Dover, VT on our (now annual?) Snowbunnies trip. This crash was late in the day on a wide open trail. I accidentally disengaged my heelside edge for a moment, causing me to rotate slightly clockwise and slide laterally. Moments later, my heelside edge caught again, now on the downhill side, causing me to quickly flip backwards onto my head … thankfully I was wearing a helmet. After that I can’t recall what exactly happened, but I know that it involved a lot of tumbling which my right ankle just couldn’t weather. (more…)

(note: I’m awaiting the HR sensor, this is mostly outward forces)

I’m logging the acceleration forces at the handlebars of my bicycle while riding through New York City. The body has roughly three contact points with a bicycle, the hands at the handlebars, the “seat” at the saddle, and the feet at the pedals. The downward force of the rider’s weight and pedaling force and the upward forces of the bicycle rolling over uneven ground are distributed over these three points. I was interested to see just what kind of forces are “pushing back” that I may not be aware of, myself lost in the act of simply keeping the bicycle upright and safely navigating through traffic.

To contextualize the raw accelerometer data I also tracking GPS location and eventually geocoding the raw data in software. The bicycle sensors are being transmitted via Bluetooth to a mobile phone and the data is logged with a custom written (but now open-source!) python script. Below is the first draft of the visualization. (more…)

I’ve heard about openFrameworks 0.6 (as of yet unreleased) and specifically efforts to support building apps using oF for the iPhone. There are a few developers who have published videos of their efforts and even several apps in the AppStore.

Memo Atken has a very informative article about setting up the prerelease 0.6 version for use with the ofxiPhone addon. However, there is a HUGE simplification of the steps necessary for getting the supporting libraries installed for use with iPhone.

Update: freetype/freeimage building instructions have been posted. (more…)